Live Crown Ratio as an Indicator for Tree Vigor and Stability of Turkey Oak (Quercus cerris L.): A Case Study

Stability of forests represents a significant objective for climate change mitigation. As stand stability is influenced by the stability of individual trees, promoting stable trees is vital for a sustainable forest management. However, inside stands, trees experience intense competition. As a result, the crown recedes and diameter growth is affected, the trees becoming slender and more susceptible to biotic and abiotic disturbances. Finding effective indicators for tree vigor and stability is therefore important. This study aimed to assess the performance of the live crown ratio as an indicator in deciding the timing of tending operations for obtaining and maintaining vigorous Turkey oak trees. Live crown ratio (LCR) and height to diameter ratio (HDR) were determined for 80 sampled Turkey oak trees. A threshold of 100 for HDR was chosen to classify trees as slender or not slender. Next, conditional inference tree and logistic regression were used to determine the LCR threshold value where trees become slender. As the sample included small trees, using breast height to measure diameter may have affected the results. Therefore, small and large trees were also analyzed separately. For the entire dataset, the methods reached quite different results (LCR threshold of 0.371 for conditional inference tree and of 0.434 for the logistic regression), and relatively high values compared to the literature. For tall trees (height > 12.5 m), the methods reached similar results: 0.386 for the conditional inference tree and 0.382 for the logistic regression. For small trees (height < 12.5 m), the conditional inference tree method could not calculate any LCR threshold estimate, while the one from the logistic regression was unrealistically large (0.628). This confirms that using DBH for small trees to compute slenderness brings systematic errors. The live crown ratio was a good indicator of growth vigor for Turkey oak trees. Therefore, for stable trees (HDR < 100), a LCR of 0.36–0.39 must be maintained and could be used to decide the timing for thinning in Turkey oak stands.

Evaluation of algorithms using administrative health and structured electronic medical record data to determine breast and colorectal cancer recurrence in a Canadian province

Background Algorithms that use administrative health and electronic medical record (EMR) data to determine cancer recurrence have the potential to replace chart reviews. This study evaluated algorithms to determine breast and colorectal cancer recurrence in a Canadian province with a universal health care system. Methods Individuals diagnosed with stage I-III breast or colorectal cancer diagnosed from 2004 to 2012 in Manitoba, Canada were included. Pre-specified and conditional inference tree algorithms using administrative health and structured EMR data were developed. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) correct classification, and scaled Brier scores were measured. Results The weighted pre-specified variable algorithm for the breast cancer validation cohort (N = 1181, 167 recurrences) demonstrated 81.1% sensitivity, 93.2% specificity, 61.4% PPV, 97.4% NPV, 91.8% correct classification, and scaled Brier score of 0.21. The weighted conditional inference tree algorithm demonstrated 68.5% sensitivity, 97.0% specificity, 75.4% PPV, 95.8% NPV, 93.6% correct classification, and scaled Brier score of 0.39. The weighted pre-specified variable algorithm for the colorectal validation cohort (N = 693, 136 recurrences) demonstrated 77.7% sensitivity, 92.8% specificity, 70.7% PPV, 94.9% NPV, 90.1% correct classification, and scaled Brier score of 0.33. The conditional inference tree algorithm demonstrated 62.6% sensitivity, 97.8% specificity, 86.4% PPV, 92.2% NPV, 91.4% correct classification, and scaled Brier score of 0.42. Conclusions Algorithms developed in this study using administrative health and structured EMR data to determine breast and colorectal cancer recurrence had moderate sensitivity and PPV, high specificity, NPV, and correct classification, but low accuracy. The accuracy is similar to other algorithms developed to classify recurrence only (i.e., distinguished from second primary) and inferior to algorithms that do not make this distinction. The accuracy of algorithms for determining cancer recurrence only must improve before replacing chart reviews.

Transarterial Chemoembolization in Treatment-Naïve and Recurrent Hepatocellular Carcinoma: A Propensity-Matched Outcome and Risk Signature Analysis

ObjectivesThe purpose of this study was to evaluate the efficacy and safety of transarterial chemoembolization (TACE) in the treatment of patients with treatment-naïve hepatocellular carcinoma (TN-HCC) and recurrent HCC (R-HCC). In addition, risk signature analysis was performed to accurately assess patients’ recurrence and survival.MethodsThis retrospective study assessed the consecutive medical records of TN-HCC and R-HCC patients from January 2014 to December 2018. In order to reduce the patient selection bias, propensity score matching (PSM) analysis was applied. Conditional inference tree was used to establish a risk signature.ResultsA total of 401 eligible patients were included in our study, including 346 patients in the TN-HCC group and 55 patients in the R-HCC group. Forty-seven pairs of patients were chosen after the PSM analysis. Before the PSM analysis, the objective tumor regression (ORR) and disease control rate (DCR) of R-HCC patients were better than that of TN-HCC patients; however, after the PSM analysis, there was no significant difference in the ORR and DCR between the two groups (P&gt;0.05). Before the PSM analysis, the median overall survival (OS) and progression-free survival (PFS) in the R-HCC group were significantly greater than those of the TN-HCC group (OS: 24 months vs. 18 months, P =0.004; PFS: 9 months vs. 6 months, P =0.012). However, after the PSM analysis, the median OS and PFS in the R-HCC group were inferior to those in the TN-HCC group (OS: 24 months vs. 33 months, P= 0.0035; PFS: 10 months vs. 12 months, P = 0.01). The conditional inference tree divided patients into different subgroups according to tumor size, BCLC stage, and TACE sessions and shared different hazards ratio to recurrence or survival.ConclusionPatients with R-HCC treated with TACE achieved satisfactory results, although survival after the PSM analysis was not as good as in the TN-HCC group. In addition, risk signature based on conditional inference tree analysis can more accurately predict the recurrence and survival in both groups of patients.

Rising Stars and Underdogs: The Role Race and Parental Education Play in Predicting Mentorship

Research suggests that youth with more financial and social resources are more likely to have access to mentorship. Conversely, the rising star hypothesis posits that youth who show promise through their individual successes are more likely to be mentored. Utilizing a nationally representative sample ( N = 4,882), we tested whether demographic characteristics (e.g., race, SES) or personal resources (e.g., academic/social success) are better predictors of receiving mentorship. Regression analyses suggested that demographic, contextual, and individual characteristics all significantly predicted access to mentorship, specifically by non-familial mentors. However, conditional inference tree models that explored the interaction of mentorship predictors by race showed that individual characteristics mattered less for Black and Latino/a youth. Therefore, the rising star hypothesis may hold true for White youth, but the story of mentoring is more complicated for youth of color. Findings highlight the implications of Critical Race Theory for mentoring research and practice.

Indicators for assessing state fiscal policy in the context of promoting sustainable development

The article proposes a scientific and methodological approach to assessing the level of promotion of fiscal policy of sustainable development, uniting a set of indicators of realization in the country of 17 SDGs. Applying the principle of the hierarchy of diagnostic knowledge, the assessment of the level of support for fiscal policies for sustainable development is presented through the linguistic variable, with the separation of measures of influence on the economic, social and environmental blocs. The hierarchical relationship between the state parameters and the level of support for fiscal policies for sustainable development is graphically presented as a logical inference tree The approach has been tested by measuring the level of promotion of fiscal policies for sustainable development in Ukraine for two scenarios, This will make it possible to predict the impact of the country’s policies. According to the results of the study, it has been proved that the level of support for the fiscal policy of sustainable development in Ukraine suppresses the implementation of 17 SDGs, and the nature of the changes tends to a pessimistic development scenario, and the total index for promoting fiscal policies for sustainable development decreases annually.

Effect of Postoperative Radiotherapy in Thymoma Patients: A SEER-Based Study

<b><i>Introduction:</i></b> The role of postoperative radiation therapy (PORT) for thymoma is under debate, especially in patients aged ≥60 years with an advanced stage (Masaoka stages III and IV). We aimed to evaluate the efficacy of PORT for thymoma in a population-based registry. <b><i>Methods:</i></b> A retrospective analysis of the Surveillance, Epidemiology, and End Results (SEER) database was conducted to compare the outcomes of thymoma patients with or without PORT. The primary outcomes were overall survival (OS) and cancer-specific survival (CSS). Conditional inference tree analyses were performed for risk classification according to the study variables. Cox regression was performed to evaluate the prognostic effect of PORT in the specific subgroups. <b><i>Results:</i></b> A total of 2,236 patients were included. The conditional inference tree analysis identified that an age ≥60, a Masaoka stage ≥3, and the year of diagnosis were important factors when classifying patients into prognostic subgroups. PORT was found to be a protective predictor of OS in patients aged ≥60 years, those with a Masaoka stage III—IV, and those diagnosed after 2005. Further subgroup analyses revealed that PORT was significantly associated with a better OS (HR = 0.77) in patients aged ≥60 years, whereas it was not significantly associated with CSS. <b><i>Conclusions:</i></b> An older age (≥60 years) is critical for predicting survival outcomes in thymoma patients. Moreover, patients aged ≥60 years could benefit from PORT in terms of OS.

Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques

The foot strike pattern performed during running is an important variable for runners, performance practitioners, and industry specialists. Versatile, wearable sensors may provide foot strike information while encouraging the collection of diverse information during ecological running. The purpose of the current study was to predict foot strike angle and classify foot strike pattern from LoadsolTM wearable pressure insoles using three machine learning techniques (multiple linear regression―MR, conditional inference tree―TREE, and random forest―FRST). Model performance was assessed using three-dimensional kinematics as a ground-truth measure. The prediction-model accuracy was similar for the regression, inference tree, and random forest models (RMSE: MR = 5.16°, TREE = 4.85°, FRST = 3.65°; MAPE: MR = 0.32°, TREE = 0.45°, FRST = 0.33°), though the regression and random forest models boasted lower maximum precision (13.75° and 14.3°, respectively) than the inference tree (19.02°). The classification performance was above 90% for all models (MR = 90.4%, TREE = 93.9%, and FRST = 94.1%). There was an increased tendency to misclassify mid foot strike patterns in all models, which may be improved with the inclusion of more mid foot steps during model training. Ultimately, wearable pressure insoles in combination with simple machine learning techniques can be used to predict and classify a runner’s foot strike with sufficient accuracy.

Evaluation of algorithms using administrative health and structured electronic medical record data to determine breast and colorectal cancer recurrence in a Canadian province

Background: Algorithms that use administrative health and electronic medical record (EMR) data to determine cancer recurrence have the potential to replace chart reviews. This study evaluated algorithms to determine breast and colorectal cancer recurrence in a Canadian province with a universal health care system.Methods: Individuals diagnosed with stage I-III breast or colorectal cancer diagnosed from 2004 to 2012 in Manitoba, Canada were included. Pre-specified and conditional inference tree algorithms using administrative health and structured EMR data were developed. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) correct classification, and scaled Brier scores were measured.Results: The weighted pre-specified variable algorithm for the breast cancer validation cohort (N=1181, 167 recurrences) demonstrated 81.1% sensitivity, 93.2% specificity, 61.4% PPV, 97.4% NPV, 91.8% correct classification, and scaled Brier score of 0.21. The weighted conditional inference tree algorithm demonstrated 68.5% sensitivity, 97.0% specificity, 75.4% PPV, 95.8% NPV, 93.6% correct classification, and scaled Brier score of 0.39. The weighted pre-specified variable algorithm for the colorectal validation cohort (N=693, 136 recurrences) demonstrated 77.7% sensitivity, 92.8% specificity, 70.7% PPV, 94.9% NPV, 90.1% correct classification, and scaled Brier score of 0.33. The conditional inference tree algorithm demonstrated 62.6% sensitivity, 97.8% specificity, 86.4% PPV, 92.2% NPV, 91.4% correct classification, and scaled Brier score of 0.42. Conclusions: Algorithms using administrative health and structured EMR data to determine breast and colorectal cancer recurrence had moderate sensitivity and PPV, high specificity, NPV, and correct classification, but low accuracy. Algorithms for determining cancer recurrence must improve before replacing chart reviews.